A big data MapReduce framework for fault diagnosis in cloud-based manufacturing

This research develops a MapReduce framework for automatic pattern recognition based on fault diagnosis by solving data imbalance problem in a cloud-based manufacturing (CBM). Fault diagnosis in a CBM system significantly contributes to reduce the product testing cost and enhances manufacturing quality. One of the major challenges facing the big data analytics in CBM is handling of data-sets, which are highly imbalanced in nature due to poor classification result when machine learning techniques are applied on such data-sets. The framework proposed in this research uses a hybrid approach to deal with big data-set for smarter decisions. Furthermore, we compare the performance of radial basis function-based Support Vector Machine classifier with standard techniques. Our findings suggest that the most important task in CBM is to predict the effect of data errors on quality due to highly imbalance unstructured data-set. The proposed framework is an original contribution to the body of literature, where our proposed MapReduce framework has been used for fault detection by managing data imbalance problem appropriately and relating it to firm's profit function. The experimental results are validated using a case study of steel plate manufacturing fault diagnosis, with crucial performance matrices such as accuracy, specificity and sensitivity. A comparative study shows that the methods used in the proposed framework outperform the traditional ones.